upload code

Project-scriptv1.2.R

@@ -0,0 +1,112 @@
+# Telco Customer Churn Analysis Script
+
+# Load required libraries
+
+library(ggplot2)
+library(dplyr)
+library(rpart)
+library(e1071)
+library(caret)
+library(pROC)
+
+# Load dataset
+
+telco <- read.csv("Telco-Customer-Churn.csv", stringsAsFactors = TRUE)
+telco\$TotalCharges <- as.numeric(as.character(telco\$TotalCharges))
+telco <- telco\[!is.na(telco\$TotalCharges), ]
+telco\$Churn <- factor(telco\$Churn, levels = c("No", "Yes"))
+
+# Exploratory Data Visualizations
+
+# Histogram for numeric variables
+
+numeric\_vars <- c("tenure", "MonthlyCharges", "TotalCharges")
+for (var in numeric\_vars) {
+  p <- ggplot(telco, aes\_string(x = var)) +
+    geom\_histogram(binwidth = 10, fill = "skyblue", color = "black") +
+    labs(title = paste("Histogram of", var), x = var, y = "Frequency") +
+    theme\_minimal()
+  ggsave(paste0("hist\_", var, ".png"), plot = p)
+}
+
+# Bar plot for churn
+
+p\_churn <- ggplot(telco, aes(x = Churn, fill = Churn)) +
+  geom\_bar() +
+  labs(title = "Churn Distribution", x = "Churn", y = "Count") +
+  theme\_minimal()
+ggsave("bar\_churn.png", plot = p\_churn)
+
+# Boxplot of MonthlyCharges by Churn
+
+p\_box <- ggplot(telco, aes(x = Churn, y = MonthlyCharges, fill = Churn)) +
+  geom\_boxplot() +
+  labs(title = "Monthly Charges by Churn", x = "Churn", y = "Monthly Charges") +
+  theme\_minimal()
+ggsave("boxplot\_monthlycharges\_churn.png", plot = p\_box)
+
+# Split data into training, validation1, and validation2
+
+set.seed(100)
+n <- nrow(telco)
+train.index <- sample(1\:n, size = round(0.70 \* n))
+remaining.index <- setdiff(1\:n, train.index)
+valid1.index <- sample(remaining.index, size = round(0.15 \* n))
+valid2.index <- setdiff(remaining.index, valid1.index)
+
+train.df <- telco\[train.index, ]
+valid1.df <- telco\[valid1.index, ]
+valid2.df <- telco\[valid2.index, ]
+
+# Logistic regression model (simplified)
+
+logit.reg <- glm(Churn \~ SeniorCitizen + Dependents + tenure + MultipleLines + InternetService + Contract +
+                   PaperlessBilling + PaymentMethod + MonthlyCharges + TotalCharges,
+                 data = train.df, family = "binomial")
+summary(logit.reg)
+
+# Evaluate on validation set
+
+valid1\_pred\_probs <- predict(logit.reg, newdata = valid1.df, type = "response")
+valid1\_pred <- factor(ifelse(valid1\_pred\_probs > 0.5, "Yes", "No"), levels = c("No", "Yes"))
+logit\_conf <- confusionMatrix(valid1\_pred, valid1.df\$Churn, positive = "Yes")
+logit\_roc <- roc(response = valid1.df\$Churn, predictor = valid1\_pred\_probs)
+
+# Decision Tree model
+
+dt\_model <- rpart(Churn \~ tenure + MonthlyCharges + TotalCharges + SeniorCitizen, data = train.df, method = "class")
+dt\_pred <- predict(dt\_model, valid1.df, type = "class")
+dt\_conf <- confusionMatrix(dt\_pred, valid1.df\$Churn)
+
+# Naive Bayes model
+
+nb\_model <- naiveBayes(Churn \~ tenure + MonthlyCharges + TotalCharges + SeniorCitizen, data = train.df)
+nb\_pred <- predict(nb\_model, valid1.df)
+nb\_probs <- predict(nb\_model, valid1.df, type = "raw")
+nb\_conf <- confusionMatrix(nb\_pred, valid1.df\$Churn)
+nb\_roc <- roc(response = valid1.df\$Churn, predictor = nb\_probs\[,"Yes"])
+
+# Print evaluations
+
+cat("\nLogistic Regression Confusion Matrix:\n")
+print(logit\_conf)
+cat("\nAUC (Logistic):", auc(logit\_roc), "\n")
+
+cat("\nDecision Tree Confusion Matrix:\n")
+print(dt\_conf)
+
+cat("\nNaive Bayes Confusion Matrix:\n")
+print(nb\_conf)
+cat("\nAUC (Naive Bayes):", auc(nb\_roc), "\n")
+
+# Save ROC curve plots
+
+png("logistic\_roc\_curve.png")
+plot(logit\_roc, main = "ROC Curve - Logistic Regression", col = "darkgreen")
+dev.off()
+
+png("naive\_bayes\_roc\_curve.png")
+plot(nb\_roc, main = "ROC Curve - Naive Bayes", col = "blue")
+dev.off()
+
+

Project/Project2.R

@@ -0,0 +1,111 @@
+# Telco Customer Churn Analysis Script
+
+# Load required libraries
+
+library(ggplot2)
+library(dplyr)
+library(rpart)
+library(e1071)
+library(caret)
+library(pROC)
+
+# Load dataset
+
+telco <- read.csv("Telco-Customer-Churn.csv", stringsAsFactors = TRUE)
+telco\$TotalCharges <- as.numeric(as.character(telco\$TotalCharges))
+telco <- telco\[!is.na(telco\$TotalCharges), ]
+telco\$Churn <- factor(telco\$Churn, levels = c("No", "Yes"))
+
+# Exploratory Data Visualizations
+
+# Histogram for numeric variables
+
+numeric\_vars <- c("tenure", "MonthlyCharges", "TotalCharges")
+for (var in numeric\_vars) {
+p <- ggplot(telco, aes\_string(x = var)) +
+geom\_histogram(binwidth = 10, fill = "skyblue", color = "black") +
+labs(title = paste("Histogram of", var), x = var, y = "Frequency") +
+theme\_minimal()
+ggsave(paste0("hist\_", var, ".png"), plot = p)
+}
+
+# Bar plot for churn
+
+p\_churn <- ggplot(telco, aes(x = Churn, fill = Churn)) +
+geom\_bar() +
+labs(title = "Churn Distribution", x = "Churn", y = "Count") +
+theme\_minimal()
+ggsave("bar\_churn.png", plot = p\_churn)
+
+# Boxplot of MonthlyCharges by Churn
+
+p\_box <- ggplot(telco, aes(x = Churn, y = MonthlyCharges, fill = Churn)) +
+geom\_boxplot() +
+labs(title = "Monthly Charges by Churn", x = "Churn", y = "Monthly Charges") +
+theme\_minimal()
+ggsave("boxplot\_monthlycharges\_churn.png", plot = p\_box)
+
+# Split data into training, validation1, and validation2
+
+set.seed(100)
+n <- nrow(telco)
+train.index <- sample(1\:n, size = round(0.70 \* n))
+remaining.index <- setdiff(1\:n, train.index)
+valid1.index <- sample(remaining.index, size = round(0.15 \* n))
+valid2.index <- setdiff(remaining.index, valid1.index)
+
+train.df <- telco\[train.index, ]
+valid1.df <- telco\[valid1.index, ]
+valid2.df <- telco\[valid2.index, ]
+
+# Logistic regression model (simplified)
+
+logit.reg <- glm(Churn \~ SeniorCitizen + Dependents + tenure + MultipleLines + InternetService + Contract +
+PaperlessBilling + PaymentMethod + MonthlyCharges + TotalCharges,
+data = train.df, family = "binomial")
+summary(logit.reg)
+
+# Evaluate on validation set
+
+valid1\_pred\_probs <- predict(logit.reg, newdata = valid1.df, type = "response")
+valid1\_pred <- factor(ifelse(valid1\_pred\_probs > 0.5, "Yes", "No"), levels = c("No", "Yes"))
+logit\_conf <- confusionMatrix(valid1\_pred, valid1.df\$Churn, positive = "Yes")
+logit\_roc <- roc(response = valid1.df\$Churn, predictor = valid1\_pred\_probs)
+
+# Decision Tree model
+
+dt\_model <- rpart(Churn \~ tenure + MonthlyCharges + TotalCharges + SeniorCitizen, data = train.df, method = "class")
+dt\_pred <- predict(dt\_model, valid1.df, type = "class")
+dt\_conf <- confusionMatrix(dt\_pred, valid1.df\$Churn)
+
+# Naive Bayes model
+
+nb\_model <- naiveBayes(Churn \~ tenure + MonthlyCharges + TotalCharges + SeniorCitizen, data = train.df)
+nb\_pred <- predict(nb\_model, valid1.df)
+nb\_probs <- predict(nb\_model, valid1.df, type = "raw")
+nb\_conf <- confusionMatrix(nb\_pred, valid1.df\$Churn)
+nb\_roc <- roc(response = valid1.df\$Churn, predictor = nb\_probs\[,"Yes"])
+
+# Print evaluations
+
+cat("\nLogistic Regression Confusion Matrix:\n")
+print(logit\_conf)
+cat("\nAUC (Logistic):", auc(logit\_roc), "\n")
+
+cat("\nDecision Tree Confusion Matrix:\n")
+print(dt\_conf)
+
+cat("\nNaive Bayes Confusion Matrix:\n")
+print(nb\_conf)
+cat("\nAUC (Naive Bayes):", auc(nb\_roc), "\n")
+
+# Save ROC curve plots
+
+png("logistic\_roc\_curve.png")
+plot(logit\_roc, main = "ROC Curve - Logistic Regression", col = "darkgreen")
+dev.off()
+
+png("naive\_bayes\_roc\_curve.png")
+plot(nb\_roc, main = "ROC Curve - Naive Bayes", col = "blue")
+dev.off()
+

Project/Project_code.R

@@ -0,0 +1,104 @@
+# Load necessary libraries
+library(forecast)
+library(ggplot2)
+library(gplots)
+library(reshape)
+library(GGally)
+library(MASS)
+library(naniar)
+library(psych)
+
+# Load dataset
+setwd('/home/anpham/Nextcloud/cpp/Data Mining/Project')
+telco.df <- read.csv("Telco-Customer-Churn.csv", stringsAsFactors = FALSE)
+
+# Display first few rows
+head(telco.df)
+
+# Check structure
+str(telco.df)
+
+# Summary statistics
+describe(telco.df)
+
+# Convert categorical variables to factors
+categorical_vars <- c("gender", "SeniorCitizen", "Partner", "Dependents", "PhoneService", 
+                      "MultipleLines", "InternetService", "OnlineSecurity", "OnlineBackup", 
+                      "DeviceProtection", "TechSupport", "StreamingTV", "StreamingMovies", 
+                      "Contract", "PaperlessBilling", "PaymentMethod", "Churn")
+telco.df[categorical_vars] <- lapply(telco.df[categorical_vars], factor)
+
+# Convert TotalCharges to numeric and handle missing values
+telco.df$TotalCharges <- as.numeric(telco.df$TotalCharges)
+telco.df$TotalCharges[is.na(telco.df$TotalCharges)] <- 0
+
+# Create new feature: Average Monthly Spend
+telco.df$AvgMonthlySpend <- with(telco.df, ifelse(tenure > 0, TotalCharges / tenure, MonthlyCharges))
+
+# Categorize tenure into groups
+telco.df$TenureCategory <- cut(telco.df$tenure, 
+                               breaks = c(-Inf, 12, 48, Inf), 
+                               labels = c("Short-Term", "Mid-Term", "Long-Term"))
+
+# Check missing values
+sum(is.na(telco.df))
+colSums(is.na(telco.df))
+
+# Visualize missing values
+gg_miss_var(telco.df)
+
+# Boxplots to detect outliers
+ggplot(telco.df, aes(y = MonthlyCharges)) + 
+  geom_boxplot(fill = "skyblue") +
+  ggtitle("Boxplot of Monthly Charges") +
+  theme_minimal()
+
+ggplot(telco.df, aes(y = TotalCharges)) + 
+  geom_boxplot(fill = "lightcoral") +
+  ggtitle("Boxplot of Total Charges") +
+  theme_minimal()
+
+# Histogram for numeric variables
+hist(telco.df$TotalCharges, col = "blue", main = "Distribution of TotalCharges")
+hist(telco.df$MonthlyCharges, col = "lightblue", border = "black", main="Distribution of Monthly Charges")
+
+# Density plot
+plot(density(telco.df$MonthlyCharges, na.rm = TRUE), col = "red", main = "Density of MonthlyCharges")
+
+# Boxplot of Monthly Charges by Churn status
+boxplot(telco.df$MonthlyCharges ~ telco.df$Churn, 
+        main = "Monthly Charges by Churn Status", 
+        xlab = "Churn (Yes/No)", 
+        ylab = "Monthly Charges", 
+        col = c("red", "blue"))
+
+# Scatter plot of tenure vs Monthly Charges
+plot(telco.df$tenure, telco.df$MonthlyCharges, 
+     xlab = "Tenure (Months)", 
+     ylab = "Monthly Charges ($)", 
+     main = "Tenure vs. Monthly Charges",
+     col = "blue", pch = 16)
+
+# Scatter plot of tenure vs churn with jitter
+ggplot(telco.df, aes(x = Churn, y = tenure, color = Churn)) +
+  geom_jitter(width = 0.2, alpha = 0.6) +  
+  labs(title = "Tenure by Churn Status",
+       x = "Churn (Yes/No)",
+       y = "Tenure (Months)") +
+  theme_minimal()
+
+# Convert Tenure to years
+telco.df$TenureYears <- telco.df$tenure / 12
+
+# Ensure "DSL" is properly recognized in InternetService
+dsl_data <- subset(telco.df, InternetService == "DSL", select = c(TenureYears, MonthlyCharges))
+
+# Scatter plot for DSL customers
+ggplot(dsl_data, aes(x = TenureYears, y = MonthlyCharges)) +
+  geom_point(color = "blue", alpha = 0.6) +  
+  labs(title = "Monthly Charges vs. Tenure (DSL Customers)",
+       x = "Tenure (Years)",
+       y = "Monthly Charges ($)") +
+  theme_minimal()
+
+

Project/tempt-code.R

@@ -0,0 +1,38 @@
+# Telco Customer Churn Analysis Script
+
+# Load required libraries
+library(ggplot2)
+library(dplyr)
+library(rpart)
+library(e1071)
+library(caret)
+library(pROC)
+
+# Load dataset
+telco <- read.csv("Telco-Customer-Churn.csv", stringsAsFactors = TRUE)
+
+telco$TotalCharges <- as.numeric(as.character(telco$TotalCharges))
+telco <- telco[!is.na(telco$TotalCharges), ]
+
+telco$Churn <- factor(telco$Churn, levels = c("No", "Yes"))
+
+# Split data
+set.seed(42)
+trainIndex <- createDataPartition(telco$Churn, p = 0.7, list = FALSE)
+train <- telco[trainIndex, ]
+test <- telco[-trainIndex, ]
+
+# Decision Tree model
+dt_model <- rpart(Churn ~ tenure + MonthlyCharges + TotalCharges + SeniorCitizen,
+                  data = train, method = "class")
+dt_pred <- predict(dt_model, test, type = "class")
+dt_conf <- confusionMatrix(dt_pred, test$Churn)
+
+# Naive Bayes model
+nb_model <- naiveBayes(Churn ~ tenure + MonthlyCharges + TotalCharges + SeniorCitizen,
+                       data = train)
+nb_pred <- predict(nb_model, test)
+nb_conf <- confusionMatrix(nb_pred, test$Churn)
+
+# ROC
+